For the purpose of generating fluoroscopic X-ray images during guidewire and catheter navigation, or during the insertion of a stent, and during vessel imaging, it is usual to use the lowest possible dose. Due to this low dose, the signal-to-noise ratio is very low, so that the image quality is very severely limited. Noise reduction filters are frequently used to improve the image quality. If these filter are given unsuitable parameter values, this can have the effect of blurring the structures contained in the image, which can result in a loss of information in the X-ray image. Another possible unfavorable effect is the generation of artifacts, i.e. of new structures in the image, which represents the insertion of erroneous information. In order to avoid these effects and the associated loss of information, the noise reduction filters must be suitably parameterized. With many noise reduction filters, multiscalar image decomposition is used to enable structures with different frequencies to be processed differently.
If the individual bandpasses are differently parameterized, the result is that adjustments require a major effort. In addition, it ought to be possible to adapt the parameter appropriately for data inputs which change due to the voltage (kV), fatness of the patient etc., to achieve a high effectiveness of the noise reduction filter and thus ensure a constant image quality.
It is possible to select the parameters for a filtering function as follows.
1. A first possibility is to adjust the parameters manually. In doing so, relevant regions are selected and measured for the noises and structure of the individual bands in an image. The parameters are then selected and fixedly set by reference to these measured values
2. An alternative to this is to perform automated measurements on images, and from these to draw conclusions about suitable parameters. For measurement purposes, the following are conceivable approaches:                A measurement is performed on the entire image on the basis of an estimated structure [Hensel06]. Here, the structure is estimated using a smoothing filter, and the noise is calculated as the difference between the original image and the structure image: noise image=original image−structure image.        
This method is not suitable for bandpass data, because only high-frequency noise can be measured and thus no conclusions are possible about the low frequency portion of the noise.                A second possibility is a partial measurement, in which only relevant regions of the image are considered. One possibility for realizing such a method is to subdivide the image into blocks. Such methods are known from [Olsen93]. Here, the input data is split up into blocks of the same size, and for each of them the standard deviation is calculated. It is assumed that the blocks with the lowest standard deviation contain no structure, but only noise. They are therefore used for noise measurement. These methods are only suitable for signal-independent noise, because they have a tendency to select those blocks which have the lowest noise levels.        